{"article_number":"e64543","citation":{"ista":"Lagator M, Sarikas S, Steinrueck M, Toledo-Aparicio D, Bollback JP, Guet CC, Tkačik G. 2022. Predicting bacterial promoter function and evolution from random sequences. eLife. 11, e64543.","short":"M. Lagator, S. Sarikas, M. Steinrueck, D. Toledo-Aparicio, J.P. Bollback, C.C. Guet, G. Tkačik, ELife 11 (2022).","chicago":"Lagator, Mato, Srdjan Sarikas, Magdalena Steinrueck, David Toledo-Aparicio, Jonathan P Bollback, Calin C Guet, and Gašper Tkačik. “Predicting Bacterial Promoter Function and Evolution from Random Sequences.” <i>ELife</i>. eLife Sciences Publications, 2022. <a href=\"https://doi.org/10.7554/eLife.64543\">https://doi.org/10.7554/eLife.64543</a>.","mla":"Lagator, Mato, et al. “Predicting Bacterial Promoter Function and Evolution from Random Sequences.” <i>ELife</i>, vol. 11, e64543, eLife Sciences Publications, 2022, doi:<a href=\"https://doi.org/10.7554/eLife.64543\">10.7554/eLife.64543</a>.","ieee":"M. Lagator <i>et al.</i>, “Predicting bacterial promoter function and evolution from random sequences,” <i>eLife</i>, vol. 11. eLife Sciences Publications, 2022.","apa":"Lagator, M., Sarikas, S., Steinrueck, M., Toledo-Aparicio, D., Bollback, J. P., Guet, C. C., &#38; Tkačik, G. (2022). Predicting bacterial promoter function and evolution from random sequences. <i>ELife</i>. eLife Sciences Publications. <a href=\"https://doi.org/10.7554/eLife.64543\">https://doi.org/10.7554/eLife.64543</a>","ama":"Lagator M, Sarikas S, Steinrueck M, et al. Predicting bacterial promoter function and evolution from random sequences. <i>eLife</i>. 2022;11. doi:<a href=\"https://doi.org/10.7554/eLife.64543\">10.7554/eLife.64543</a>"},"volume":11,"ddc":["576"],"date_created":"2022-02-06T23:01:32Z","publication_status":"published","department":[{"_id":"CaGu"},{"_id":"GaTk"},{"_id":"NiBa"}],"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","publication":"eLife","month":"01","article_type":"original","abstract":[{"lang":"eng","text":"Predicting function from sequence is a central problem of biology. Currently, this is possible only locally in a narrow mutational neighborhood around a wildtype sequence rather than globally from any sequence. Using random mutant libraries, we developed a biophysical model that accounts for multiple features of σ70 binding bacterial promoters to predict constitutive gene expression levels from any sequence. We experimentally and theoretically estimated that 10–20% of random sequences lead to expression and ~80% of non-expressing sequences are one mutation away from a functional promoter. The potential for generating expression from random sequences is so pervasive that selection acts against σ70-RNA polymerase binding sites even within inter-genic, promoter-containing regions. This pervasiveness of σ70-binding sites implies that emergence of promoters is not the limiting step in gene regulatory evolution. Ultimately, the inclusion of novel features of promoter function into a mechanistic model enabled not only more accurate predictions of gene expression levels, but also identified that promoters evolve more rapidly than previously thought."}],"external_id":{"pmid":["35080492"],"isi":["000751104400001"]},"pmid":1,"date_published":"2022-01-26T00:00:00Z","intvolume":"        11","license":"https://creativecommons.org/licenses/by/4.0/","title":"Predicting bacterial promoter function and evolution from random sequences","oa_version":"Published Version","oa":1,"acknowledgement":"We thank Hande Acar, Nicholas H Barton, Rok Grah, Tiago Paixao, Maros Pleska, Anna Staron, and Murat Tugrul for insightful comments and input on the manuscript. This work was supported by: Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (grant number 216779/Z/19/Z) to ML; IPC Grant from IST Austria to ML and SS; European Research Council Funding Programme 7 (2007–2013, grant agreement number 648440) to JPB.","date_updated":"2023-08-02T14:09:02Z","_id":"10736","has_accepted_license":"1","year":"2022","scopus_import":"1","project":[{"grant_number":"648440","_id":"2578D616-B435-11E9-9278-68D0E5697425","name":"Selective Barriers to Horizontal Gene Transfer","call_identifier":"H2020"}],"isi":1,"article_processing_charge":"No","doi":"10.7554/eLife.64543","status":"public","ec_funded":1,"publisher":"eLife Sciences Publications","day":"26","tmp":{"short":"CC BY (4.0)","image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"publication_identifier":{"eissn":["2050-084X"]},"language":[{"iso":"eng"}],"author":[{"id":"345D25EC-F248-11E8-B48F-1D18A9856A87","first_name":"Mato","full_name":"Lagator, Mato","last_name":"Lagator"},{"last_name":"Sarikas","full_name":"Sarikas, Srdjan","first_name":"Srdjan","id":"35F0286E-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Steinrueck","full_name":"Steinrueck, Magdalena","first_name":"Magdalena"},{"full_name":"Toledo-Aparicio, David","first_name":"David","last_name":"Toledo-Aparicio"},{"full_name":"Bollback, Jonathan P","first_name":"Jonathan P","last_name":"Bollback","id":"2C6FA9CC-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-4624-4612"},{"last_name":"Guet","full_name":"Guet, Calin C","first_name":"Calin C","id":"47F8433E-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-6220-2052"},{"first_name":"Gašper","full_name":"Tkačik, Gašper","last_name":"Tkačik","orcid":"0000-0002-6699-1455","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87"}],"file_date_updated":"2022-02-07T07:14:09Z","file":[{"file_name":"2022_ELife_Lagator.pdf","file_size":5604343,"relation":"main_file","creator":"cchlebak","date_updated":"2022-02-07T07:14:09Z","content_type":"application/pdf","access_level":"open_access","file_id":"10739","date_created":"2022-02-07T07:14:09Z","checksum":"decdcdf600ff51e9a9703b49ca114170","success":1}],"quality_controlled":"1","type":"journal_article"}